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1
ichigaku.takigawa@riken.jp
33 AIP Open Seminar
2021 7 14
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https://itakigawa.github.io/
ͨ ͖ ͕ Θ ͍ ͪ ͕ ͘
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CC1CCNO1
Representation
Learning
…
NCc1ccoc1.S=(Cl)Cl>>[RX_5]S=C=NCc1ccoc1
Task-Specific
Head
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4
Amide
module
Proline
module
Oxazoline
module
Phenyl
Carboxyl
Methyl
Tert-butyl
Isoprophyl
Trifluoromethyl
Benzyl
J....
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5
https://pubchem.ncbi.nlm.nih.gov/#query=C12CC(C(CC1)C2)C
‫ີݫ‬Ұக‫ࡧݕ‬
Norbornane
ྨࣅੑ‫ࡧݕ‬ ্෦ߏ଄‫ࡧݕ‬
෦෼ߏ଄‫ࡧݕ‬ ཱମྨࣅੑ‫ࡧݕ‬
...
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6
https://www.molgen.de/online.html
EXAMPLE 5: Generate all (theoretically possible)
structures of mass ≤ 40 with ele...
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O
N
N
N
H
NH
N
N
N
CH3
CH3
https://en.wikipedia.org/wiki/Molecular_dynamics
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8
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CH3
N
N
H
N
H
H3C
N
<latexit sha1_base64="tiacEhQgmTkomNeV5LHmrY6hmbk=">AAAChnichVHLTsJAFD3UF+ID1I2JGyLBuCJTXxhXRDc...
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CH3
N
N
H
N
H
H3C
N
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Stumpfe and Bajorath, J Med Chem (2012) https://doi.org/10.1021/jm201706b
Activity cliffs Selectivity cliffs
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CH3
N
N
H
N
H
H3C
N
0.739
H3C
H3C
NH
O
N
O
N
O
CH3
O N
NH2
O
CH3
Br
CH3
N
H3C
H
N
S
N
O
CH3
N
OH
CH3
CH3
N
N
N
CH3...
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• Mutagenic potency
• Carcinogenic potency
• Endocrine disruption
• Growth inhibition
• Aqueous solubility
N
NH
O
...
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=@<TRIPOS>MOLECULE
*****
13 13 0 0 0
SMALL
GASTEIGER
@<TRIPOS>ATOM
1 C -2.5458 -9.4750 0.0000 C.2 1 UNL1 0.3080
2 ...
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MDL MACCS Keys
ΑΓෳࡶͳߏ଄ಛ௃ύλʔϯΛ໢ཏͨ͠PubChem Fingerprint (881bit)ͳͲ΋
0000000000000000000000000000000000000000000000000...
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Implicit Hydrogens Explicit Hydrogens
Structural Formula
Atomic Invariants
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Kashima Kernel (Kashima+ 2003)
Weisfeiler-Lehman Kernel (Shervashidze+ 2011)
<latexit sha1_base64="wzm3ilbAnfk/uMM...
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<latexit sha1_base64="JQWY0sKtSB2gZKyDx03+dQGakWQ=">AAAClHichVHLSsNAFL2Nr1ofrQoiuCmWiqsyUVERhWIRXUkf9gFtLUmc1qFpEp...
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<latexit sha1_base64="JQWY0sKtSB2gZKyDx03+dQGakWQ=">AAAClHichVHLSsNAFL2Nr1ofrQoiuCmWiqsyUVERhWIRXUkf9gFtLUmc1qFpEp...
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Wu et al, MoleculeNet: a benchmark for molecular machine learning, Chem Sci (2017)
https://doi.org/10.1021/jm20170...
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Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties. Chem Rev, 2012, 112 (5), pp...
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CC1CCNO1
Representation
Learning
…
NCc1ccoc1.S=(Cl)Cl>>[RX_5]S=C=NCc1ccoc1
Task-Specific
Head
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24
N O
C
C
C
C
H
H
H
H
H
N O
C
C
C
C
H
H
H
H
H
(sum, mean or max)
+ attention
<latexit sha1_base64="WNEpfX6Bt3G9f3Toy...
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<latexit sha1_base64="g4+ASD58WMoop6pPZrlXxKmH4Ao=">AAAC7XichVHLahRBFL1pX3F8ZKIbwU3jEJlBGao1aHAVzMZVyGuSQDo21WXNdE...
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Duvenaud, Maclaurin, Aguilera-Iparraguirre, Gómez-Bombarell, Hirzel, Aspuru-Guzik, Adams,
Convolutional networks o...
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<latexit sha1_base64="cBeKmO56fa7C7dRhqoeO+wzPccY=">AAAChHichVHLSsNAFD2N7/po1Y3gplgqLqRMrC9cSNGNyz5sK2iRJE5raJqEZF...
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(Multihead)
Self-attention
Feed-forward NN
Add + LayerNorm
Add + LayerNorm
Transformer
GNN Layer
Embedding + Pos E...
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Strategies for Pre-training Graph Neural Networks
Hu, Liu, Gomes, Zitnik, Liang, Pande, Leskovec (ICLR 2020)
https...
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https://arxiv.org/abs/2012.15544
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Zahrt, Henle, Rose, Wang, Darrow, Denmark,
Prediction of higher-selectivity catalysts by computer-driven
workflow a...
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https://arxiv.org/abs/2104.13478
https://youtu.be/uF53xsT7mjc
https://youtu.be/w6Pw4MOzMuo
ICLR 2021 Keynote (Mich...
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N O
C
C
C
C
H
H
H
H
H
Dipole moment
Isotropic polarizability
HOMO energy
LUMO energy
Gap between HOMO and LUMO
Ele...
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1st place: 10 GNNs (12-Layer Graphormer) + 8 ExpC*s (5-Layer ExpandingConv)
73 GNNs (11-Layer LiteGEMConv with Sel...
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• the number of immediate neighbors who are
“heavy” (non-hydrogen) atoms
• the valence minus the number of hydroge...
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Continuous-Filter Convolutions
(cfconv layers)
<latexit sha1_base64="flzLPrMsSS6k1am7yfKyC95kal4=">AAACiXichVG7SgN...
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Schütt et al, SchNet. (2017) https://arxiv.org/abs/1706.08566
Satorras et al, E(n) Equivariant Graph Neural Networ...
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NeurIPS 2020 ICML 2020, 2021
ICLR 2020, 2021
• Self-Supervised Graph Transformer on Large-Scale Molecular
Data
• R...
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https://arxiv.org/abs/2102.06321 https://arxiv.org/abs/1911.10084
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CC1CCNO1
Representation
Learning
…
NCc1ccoc1.S=(Cl)Cl>>[RX_5]S=C=NCc1ccoc1
Task-Specific
Head
https://itakigawa.git...
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(2021.7) 分子のグラフ表現と機械学習の最近 Slide 1 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 2 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 3 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 4 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 5 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 6 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 7 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 8 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 9 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 10 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 11 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 12 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 13 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 14 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 15 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 16 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 17 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 18 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 19 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 20 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 21 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 22 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 23 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 24 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 25 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 26 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 27 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 28 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 29 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 30 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 31 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 32 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 33 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 34 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 35 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 36 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 37 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 38 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 39 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 40 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 41 (2021.7) 分子のグラフ表現と機械学習の最近 Slide 42
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(2021.7) 分子のグラフ表現と機械学習の最近

  1. 1. / 41 1 ichigaku.takigawa@riken.jp 33 AIP Open Seminar 2021 7 14
  2. 2. / 41 2 https://itakigawa.github.io/ ͨ ͖ ͕ Θ ͍ ͪ ͕ ͘
  3. 3. / 41 3 CC1CCNO1 Representation Learning … NCc1ccoc1.S=(Cl)Cl>>[RX_5]S=C=NCc1ccoc1 Task-Specific Head
  4. 4. / 41 4 Amide module Proline module Oxazoline module Phenyl Carboxyl Methyl Tert-butyl Isoprophyl Trifluoromethyl Benzyl J.J.Sylvester, Chemistry and Algebra, Nature, 17:284 (1878). GDB-11 (Fink+, JCIM 2007) C,N,O,F,ʹΑΔ11‫ࢠݪ‬ҎԼͷ෼ࢠͷશྻ‫ڍ‬ (2640ສ) GDB-13 (Blum+, JACS 2009) C,N,O,S,ClʹΑΔ13‫ࢠݪ‬ҎԼͷ෼ࢠͷશྻ‫ڍ‬ (9ԯ7700ສ) GDB-17 (Ruddigkeit+, JCIM 2012) C,N,O,S,ϋϩήϯʹΑΔ17‫ࢠݪ‬ҎԼͷ෼ࢠͷશྻ‫ڍ‬ (1,664ԯ) Hydrogen Proline Oxazoline Amide Ethyl Cyclohexyl adamantyl ʹ͸༷ʑͳஔ‫͕ج׵‬ೖΕΒΕΔ
  5. 5. / 41 5 https://pubchem.ncbi.nlm.nih.gov/#query=C12CC(C(CC1)C2)C ‫ີݫ‬Ұக‫ࡧݕ‬ Norbornane ྨࣅੑ‫ࡧݕ‬ ্෦ߏ଄‫ࡧݕ‬ ෦෼ߏ଄‫ࡧݕ‬ ཱମྨࣅੑ‫ࡧݕ‬ ΫΤϦߏ଄ͱಉ͡ ΫΤϦͱྨࣅ ΫΤϦߏ଄ʹ‫·ؚ‬ΕΔ ΫΤϦߏ଄Λ‫ؚ‬Ή ΫΤϦߏ଄ͱཱମతʹྨࣅ
  6. 6. / 41 6 https://www.molgen.de/online.html EXAMPLE 5: Generate all (theoretically possible) structures of mass ≤ 40 with elements, C, H, N3, O Hu, Stumpfe, Bajorath, J Med Chem (2016) https://doi.org/10.1021/acs.jmedchem.5b01746 Liu, Naderi, Alvin, Mukhopadhyay, Brylinski, JCIM (2017) https://doi.org/10.1021/acs.jcim.6b00596
  7. 7. / 41 7 O N N N H NH N N N CH3 CH3 https://en.wikipedia.org/wiki/Molecular_dynamics
  8. 8. / 41 8 <latexit sha1_base64="dwtAUUE0cfsFu6+2FLg7b109CNE=">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</latexit> ✓i <latexit sha1_base64="tkPRNIYeS8tNgbH62CO/ULi3LDw=">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</latexit> ✓j https://en.wikipedia.org/wiki/Chirality_(chemistry)
  9. 9. / 41 9 CH3 N N H N H H3C N <latexit sha1_base64="tiacEhQgmTkomNeV5LHmrY6hmbk=">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</latexit> x1 <latexit sha1_base64="4Sn0JXQ8Nli9zYaRMiYfd4a9JHg=">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</latexit> x2 <latexit sha1_base64="4+jTZNjL3Gn2jlRZoSuvOUm3czc=">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</latexit> . . . <latexit sha1_base64="9bGzB58W461U5gZXfti9NDCxbbY=">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</latexit> y Hansch-Fujita QSAR (Hammetଇͷੜ෺ֶ൛) (Hammettఆ਺) Hammettଇ <latexit sha1_base64="Rtaf0NASFdmDpXAc2f6wWTFtSe0=">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</latexit> log(K/KH) = ⇢ “Linear Free Energy Relationships (LFERs)” <latexit sha1_base64="Xiu9z9gQDsGsRYjNbuMpINCUhK0=">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</latexit> log(1/C) = 1(log P)2 + 2 log P + 3 + 4Es + Const. (Taftఆ਺) (෼഑܎਺) ੜ෺‫׆‬ੑ΍ ೱ౓
  10. 10. / 41 10 CH3 N N H N H H3C N <latexit sha1_base64="tiacEhQgmTkomNeV5LHmrY6hmbk=">AAAChnichVHLTsJAFD3UF+ID1I2JGyLBuCJTXxhXRDcuecgjQULaOmJDaZu2EJH4AyZuZeFKExfGD/AD3PgDLvgE4xITNy68lCZGiXib6Zw5c8+dM3NlU1Nth7GOTxgZHRuf8E8GpqZnZoOhufmcbdQthWcVQzOsgizZXFN1nnVUR+MF0+JSTdZ4Xq7u9fbzDW7ZqqEfOE2Tl2pSRVePVUVyiMqclsVyKMJizI3wIBA9EIEXSSP0iEMcwYCCOmrg0OEQ1iDBpq8IEQwmcSW0iLMIqe4+xzkCpK1TFqcMidgq/Su0KnqsTuteTdtVK3SKRsMiZRhR9sLuWZc9swf2yj7/rNVya/S8NGmW+1puloMXi5mPf1U1mh2cfKuGenZwjG3Xq0reTZfp3ULp6xtn7W5mJx1trbBb9kb+b1iHPdEN9Ma7cpfi6eshfmTyQi9GDRJ/t2MQ5NZi4lZsPbURSex6rfJjCctYpX7EkcA+kshS/QoucYW24BdiwqYQ76cKPk+zgB8hJL4AVGCQmw==</latexit> x1 <latexit sha1_base64="4Sn0JXQ8Nli9zYaRMiYfd4a9JHg=">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</latexit> x2 <latexit sha1_base64="4+jTZNjL3Gn2jlRZoSuvOUm3czc=">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</latexit> . . . <latexit sha1_base64="9bGzB58W461U5gZXfti9NDCxbbY=">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</latexit> y f
  11. 11. / 41 11 Stumpfe and Bajorath, J Med Chem (2012) https://doi.org/10.1021/jm201706b Activity cliffs Selectivity cliffs
  12. 12. / 41 12 CH3 N N H N H H3C N 0.739 H3C H3C NH O N O N O CH3 O N NH2 O CH3 Br CH3 N H3C H N S N O CH3 N OH CH3 CH3 N N N CH3 H3C H2N NH2 ‫ݟ‬ຊྫ͔ΒͷϓϩάϥϜੜ੒
  13. 13. / 41 13 • Mutagenic potency • Carcinogenic potency • Endocrine disruption • Growth inhibition • Aqueous solubility N NH O O H H H H H H H H H H H H H H H H H H H H H H H H H O O O O O O Cl H H H H H H H H H H H H H H H H H Br Br O P O O Br Br O Br Br H H H H H H H H H H H H H H H N S N N H H H H H H H H H H H H H H H O N O O H H H O O H H N O O Cl Cl Cl H H H H H H H N O O H H H H H H H H H N O O H H H H H H H N H N O O N O O H H H H H H H H N CH3 O O H N Cl Cl Cl Cl Cl H3C O O O O O O H3C CH3 CH2 O HN O O NH CH3 HO OH CH3 N O O CH3 N N H N H H3C N H3C H3C NH O N O N O CH3 O N NH2 O CH3 Br CH3 N H3C H N S N O CH3 N OH CH3 CH3 N N N CH3 H3C H2N NH2 H OH O HO CH3 H H O CH3 H O O H3C H H H O H3C S CH3 O H H O CH3 CH3 O O HO H3C H HO F H O H3C NH2 O N HO H O O H H O O O H3C O O O CH3 O CH3 H O CH3 H O O CH3 H H N H N O H3C O O O
  14. 14. / 41 14
  15. 15. / 41 15 =@<TRIPOS>MOLECULE ***** 13 13 0 0 0 SMALL GASTEIGER @<TRIPOS>ATOM 1 C -2.5458 -9.4750 0.0000 C.2 1 UNL1 0.3080 2 C -3.3708 -9.4750 0.0000 C.2 1 UNL1 0.2529 3 C -2.2875 -8.6917 0.0000 C.2 1 UNL1 0.3838 4 C -3.6208 -8.6917 0.0000 C.3 1 UNL1 0.2067 5 O -2.9583 -8.2042 0.0000 O.3 1 UNL1 -0.4441 6 C -4.3583 -8.3125 0.0000 C.3 1 UNL1 0.2245 7 O -1.5000 -8.4375 0.0000 O.2 1 UNL1 -0.2412 8 O -2.0583 -10.1417 0.0000 O.2 1 UNL1 -0.2764 9 O -3.8500 -10.1417 0.0000 O.2 1 UNL1 -0.2843 10 O -5.0500 -8.7542 0.0000 O.3 1 UNL1 -0.2164 11 O -3.6958 -7.0417 0.0000 O.3 1 UNL1 -0.2174 12 C -4.3958 -7.4875 0.0000 C.3 1 UNL1 0.2185 13 H -4.2083 -9.2667 0.0000 H 1 UNL1 0.0853 @<TRIPOS>BOND 1 2 1 2 2 3 1 1 3 4 2 1 4 5 3 1 5 6 4 1 6 7 3 2 7 8 1 1 8 9 2 1 9 6 10 1 10 11 12 1 11 12 6 1 12 4 13 1 13 5 4 1 OC[C@H](O)[C@H]1OC(=O)C(=C1O)O CIWBSHSKHKDKBQ-JLAZNSOCSA-N InChI=1S/C6H8O6/ c7-1-2(8)5-3(9)4(10)6(11)12-5/ h2,5,7-10H,1H2/t2-,5+/m0/s1 1 2 3 4 5 6 7 8 9 10 11 12 13 2 2 1 1 1 1 1 1 1 1 1 1 1 single single single double double double double double double single single single aromatic aromatic aromatic aromatic aromatic aromatic Kekulé Form
  16. 16. / 41 16 MDL MACCS Keys ΑΓෳࡶͳߏ଄ಛ௃ύλʔϯΛ໢ཏͨ͠PubChem Fingerprint (881bit)ͳͲ΋ 00000000000000000000000000000000000000000000000000001100 00000000000000000000000000000000010000100000000000000000 0100000100011101000101010001001111100010101011111111110 Aromatic Ring>1 [F,Cl,Br,I] Heterocycle ֤ߏ଄ಛ௃ͷ༗ແΛ ֤bit(0/1)ʹ ECFP (Extended Connectivity Fingerprint) 00001000100011001011001000001000010 HashingͰ‫ݻ‬ఆ௕ͷ bit string΁ม‫׵‬ ֤ۙ๣ߏ଄ʹuniqueͳ id൪߸Λׂ౰ͯΔ https://docs.chemaxon.com/display/docs/extended-connectivity-fingerprint-ecfp.md ୳ࡧ൒‫ܘ‬ͷ্‫ͱݶ‬Ϗοτ௕͸ύϥϝλ …
  17. 17. / 41 17 Implicit Hydrogens Explicit Hydrogens Structural Formula Atomic Invariants
  18. 18. / 41 <latexit sha1_base64="9kTSgBYDa8jzBNKjMs5WyhQ5V3k=">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</latexit> K , ! = 18 0.1 0.7 0.9 ⋮ ⋮ 1.2 0 0 1 1 1 0 1 0 0 0 0 1 1 1 0 1 1 0 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ 1 0 1 1 1 0 y
  19. 19. / 41 19 Kashima Kernel (Kashima+ 2003) Weisfeiler-Lehman Kernel (Shervashidze+ 2011) <latexit sha1_base64="wzm3ilbAnfk/uMMoZ6aWms13YM8=">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</latexit> G1 <latexit sha1_base64="FUojSoBiuD8NhDGX50StN4VMT70=">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</latexit> G2 1 1 2 2 2 3 3 4 4 5 5 1,4 1,4 2,35 2,45 2,3 3,245 3,245 4,1235 4,1135 5,234 5,234 1,4 1 6 6 8 9 7 10 10 12 11 13 13 6 … … … … 2 1 1 1 1 2 0 1 1 2 3 4 5 6 7 8 1 2 1 1 1 1 1 0 Marginalized Kernel (Tsuda+ 2002) R-Convolution Kernel (Haussler 1999) R D <latexit sha1_base64="wpyn9s0DvnQcyTF5k8mpdFbbQqA=">AAACjXichVHLSsNAFD2N7/po1Y3gplgqFaRM6hMRKbqo4KZV+8AHJYnTGpomIUkLWvwB14ILUVBwIX6AH+DGH3DhJ4hLBTcuvE0DokW9YTJnztxz58xc2dRU22HsySe0tXd0dnX3+Hv7+gcCwcGhrG1ULYVnFEMzrLws2VxTdZ5xVEfjedPiUkXWeE4urzT2czVu2aqhbzoHJt+tSCVdLaqK5BC1tRZNFsTJZCE+UQiGWYy5EWoFogfC8CJlBO+wgz0YUFBFBRw6HMIaJNj0bUMEg0ncLurEWYRUd5/jCH7SVimLU4ZEbJn+JVpte6xO60ZN21UrdIpGwyJlCBH2yG7YK3tgt+yZffxaq+7WaHg5oFluarlZCByPbLz/q6rQ7GD/S/WnZwdFzLteVfJuukzjFkpTXzs8fd1YWI/Ux9kVeyH/l+yJ3dMN9Nqbcp3m62d/+JHJC70YNUj82Y5WkI3HxNnYVHo6nFj2WtWNUYwhSv2YQwKrSCFD9XWc4BwXQkCYERaFpWaq4PM0w/gWQvITcWuSUA==</latexit> K(G1, G2)
  20. 20. / 41 20 <latexit sha1_base64="JQWY0sKtSB2gZKyDx03+dQGakWQ=">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</latexit> {Gi}n i=1 Wale N., Ning X., Karypis G. (2010) Trends in Chemical Graph Data Mining. In: Aggarwal C., Wang H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_19
  21. 21. / 41 20 <latexit sha1_base64="JQWY0sKtSB2gZKyDx03+dQGakWQ=">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</latexit> {Gi}n i=1 Wale N., Ning X., Karypis G. (2010) Trends in Chemical Graph Data Mining. In: Aggarwal C., Wang H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_19
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  24. 24. / 41 23 CC1CCNO1 Representation Learning … NCc1ccoc1.S=(Cl)Cl>>[RX_5]S=C=NCc1ccoc1 Task-Specific Head
  25. 25. / 41 24 N O C C C C H H H H H N O C C C C H H H H H (sum, mean or max) + attention <latexit sha1_base64="WNEpfX6Bt3G9f3Toyi7bd0iGAgY=">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</latexit> hi 0 @hi, M j2Ni (hi, hi, eij) 1 A
  26. 26. / 41 25 <latexit sha1_base64="g4+ASD58WMoop6pPZrlXxKmH4Ao=">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</latexit> h (l+1) i 0 @h (l) i , M j2Ni cij (h (l) j ) 1 A (Kipf and Welling, ICLR ︎2017) <latexit sha1_base64="/ysxkHppQdzDUyY3/GZwEdDJ/xs=">AAACjHichVHLLgRBFD3ae7wGG4nNxGTEalKDIEQyIRHLeRhGEOluZVT0K93Vk4yJH7C1sMCCxEJ8gA+w8QMWPkEsSWws3O7pRBDcTnWdOnXPrVN1NccQnmTssUlpbmlta+/ojHV19/T2xfsHVj3bd3Ve0m3Ddsua6nFDWLwkhTR42XG5amoGX9P2F4P9tSp3PWFbK7Lm8C1TrVhiV+iqJGp9UxMV2zF8bzueZGkWRuInyEQgiShydvwWm9iBDR0+THBYkIQNqPDo20AGDA5xW6gT5xIS4T7HIWKk9SmLU4ZK7D79K7TaiFiL1kFNL1TrdIpBwyVlAin2wK7ZC7tnN+yJvf9aqx7WCLzUaNYaWu5s9x0NFd/+VZk0S+x9qv70LLGLmdCrIO9OyAS30Bv66sHJS3G2kKqPskv2TP4v2CO7oxtY1Vf9Ks8Lp3/40cgLvRg1KPO9HT/B6ng6M5WeyE8mswtRqzowjBGMUT+mkcUycihRfRPHOMO50qtMKnPKfCNVaYo0g/gSytIHDDCTkA==</latexit> M <latexit sha1_base64="wzm3ilbAnfk/uMMoZ6aWms13YM8=">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</latexit> G1 <latexit sha1_base64="FUojSoBiuD8NhDGX50StN4VMT70=">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</latexit> G2 Xu, Hu, Leskovec, Jegelka, How powerful are graph neural networks? ICLR (2019) Kipf and Welling, Semi-supervised classification with graph convolutional networks. ICLR (2017)
  27. 27. / 41 26 Duvenaud, Maclaurin, Aguilera-Iparraguirre, Gómez-Bombarell, Hirzel, Aspuru-Guzik, Adams, Convolutional networks on graphs for learning molecular fingerprints. NIPS (2015)
  28. 28. / 41 27 <latexit sha1_base64="cBeKmO56fa7C7dRhqoeO+wzPccY=">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</latexit> 1 <latexit 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sha1_base64="bY/zJsMyfi+VfZIaAVc1VF0vNBA=">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</latexit> v1 concat update Directed MPNN (Dai et al, ICML 2016) node feature ChemProp (Yang et al, JCIM 2019) https://github.com/chemprop/chemprop Stokes et al, Cell (2020) https://doi.org/10.1016/j.cell.2020.01.021 Marchant, Nature (2020) https://doi.org/10.1038/d41586-020-00018-3
  29. 29. / 41 28 (Multihead) Self-attention Feed-forward NN Add + LayerNorm Add + LayerNorm Transformer GNN Layer Embedding + Pos Encoding A Generalization of Transformer Networks to Graphs Dwivedi & Bresson (2020) https://arxiv.org/abs/2012.09699 Do Transformers Really Perform Bad for Graph Representation? Ying et al (2021) https://arxiv.org/abs/2106.05234 Communicative Representation Learning on Attributed Molecular Graphs Song et al (2020) https://www.ijcai.org/proceedings/2020/0392.pdf Graph-BERT: Only Attention is Needed for Learning Graph Representations Zhang et al (2020) https://arxiv.org/abs/2001.05140 Veličković, Cucurull, Casanova, Romero, Liò, Bengio, Graph Attention Networks (ICLR 2018) https://arxiv.org/abs/1710.10903 Joshi, Transformers are Graph Neural Networks. (2020) https://graphdeeplearning.github.io/post/transformers-are-gnns/ Ying et al (2021) ͷGraphormer͸ KDDCup 2021ͷOpen Graph Benchmark Large-Scale Challenge(‫ޙ‬ड़)ͷGraph-level λεΫͷ༏উϞσϧͰ࢖ΘΕͨ େ‫ن‬໛σʔλͳΒάϥϑͰ΋ Transformer͸༗ޮ…!?
  30. 30. / 41 29 Strategies for Pre-training Graph Neural Networks Hu, Liu, Gomes, Zitnik, Liang, Pande, Leskovec (ICLR 2020) https://arxiv.org/abs/1905.12265 Self-Supervised Graph Transformer on Large-Scale Molecular Data Rong, Bian, Xu, Xie, Wei, Huang, Huang (NeurIPS 2020) https://arxiv.org/abs/2007.02835
  31. 31. / 41 30 https://arxiv.org/abs/2012.15544
  32. 32. / 41 31 Zahrt, Henle, Rose, Wang, Darrow, Denmark, Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science, 363(6424), 2019. https://doi.org/10.1126/science.aau5631
  33. 33. / 41 32 https://arxiv.org/abs/2104.13478 https://youtu.be/uF53xsT7mjc https://youtu.be/w6Pw4MOzMuo ICLR 2021 Keynote (Michael Bronstein) Seminar Talk (Petar Veličković)
  34. 34. / 41 33 N O C C C C H H H H H Dipole moment Isotropic polarizability HOMO energy LUMO energy Gap between HOMO and LUMO Electronic spatial extent Zero point vibrational energy Internal energy at 0K Internal energy at 298.15K Enthalpy at 298.15K Free energy at 298.15K Heat capavity at 298.15K Atomization energy at 0K Atomization energy at 298.15K Atomization enthalpy at 298.15K Atomization free energy at 298.15K Rotational constant A Rotational constant B Rotational constant C ICML 2017 https://arxiv.org/abs/1704.01212 JCTC 2017 https://doi.org/10.1021/acs.jctc.7b00577
  35. 35. / 41 34 1st place: 10 GNNs (12-Layer Graphormer) + 8 ExpC*s (5-Layer ExpandingConv) 73 GNNs (11-Layer LiteGEMConv with Self-Supervised Pretraining) 20 GNNs (32-Layer GNN with Noisy Nodes) Test MAE 0.1200 (eV) 2nd place:Test MAE 0.1204 (eV) 3rd place: Test MAE 0.1205 (eV) Results: https://ogb.stanford.edu/kddcup2021/results/#awardees_pcqm4m https://ogb.stanford.edu/kddcup2021/
  36. 36. / 41 35 • the number of immediate neighbors who are “heavy” (non-hydrogen) atoms • the valence minus the number of hydrogens • the atomic number • the atomic mass • the atomic charge • the number of attached hydrogens • whether the atom is contained in at least one ring • hydrogen-bond acceptor or not? • hydrogen-bond donor or not? • negatively ionizable or not? • positively ionizable or not? • aromatic or not? • halogen or not? Rogers and Hahn, JCIM (2005) https://doi.org/10.1021/ci100050t Faber et al, JCTC (2017) https://doi.org/10.1021/acs.jctc.7b00577
  37. 37. / 41 36 Continuous-Filter Convolutions (cfconv layers) <latexit sha1_base64="flzLPrMsSS6k1am7yfKyC95kal4=">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</latexit> dij rbf(γ,μ) #rbf MLP dim of element-wise product Gaussian Smearing <latexit sha1_base64="CP/gGUdLE1ahITrVyosFmAESNpw=">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</latexit> xi X j2Ni xj (exp( (dij µ))) <latexit sha1_base64="sUht8Mvlx5JoWAMHabceBQWRmAs=">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</latexit> xi Schütt, Kindermans, Sauceda, Chmiela, Tkatchenko, Müller, SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. https://arxiv.org/abs/1706.08566
  38. 38. / 41 37 Schütt et al, SchNet. (2017) https://arxiv.org/abs/1706.08566 Satorras et al, E(n) Equivariant Graph Neural Networks. (2021) https://arxiv.org/abs/2102.09844 Anderson et al, Cormorant. (2019) https://arxiv.org/abs/1906.04015 Unke et al, PhysNet. (2019) https://arxiv.org/abs/1902.08408 Klicpera et al, DimeNet++. (2020) https://arxiv.org/abs/2011.14115 Fuchs et al, SE(3)-Transformers. (2021) https://arxiv.org/abs/2006.10503 Köhler et al, Equivariant Flows (Radial Field). (2020) https://arxiv.org/abs/2006.02425 Thomas et al, Tensor Field Networks. (2018) https://arxiv.org/abs/1802.08219 <latexit sha1_base64="NH4UQ68bqmsH0AzQM//vHVYIu40=">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</latexit> f(g · x) = g · f(x) <latexit sha1_base64="z65vGkIR8AznuZeRro+w9TcH+xY=">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</latexit> f(g · x) = f(x) <latexit sha1_base64="h5Nu57LzNEgsKVoc7SjFgDOfXxQ=">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</latexit> g 2 G <latexit sha1_base64="98i0QCpyFbQuYbP7ylFU0FuKpWo=">AAACjHichVG7SgNBFD1Z3/GRqI1gEwyKVZjVoKIIoiCWSTQaMRJ210lcsi92JwEN/oCthYVaKFiIH+AH2PgDFn6CWCrYWHh3syAqxrvMzpkz99w5M1d1DN0TjD1FpLb2js6u7p5ob1//QCw+OLTp2TVX43nNNmy3oCoeN3SL54UuDF5wXK6YqsG31OqKv79V566n29aGOHD4rqlULL2sa4ogars8nygUhZ3YLsWTLMWCSPwGcgiSCCNjx+9QxB5saKjBBIcFQdiAAo++HchgcIjbRYM4l5Ae7HMcIUraGmVxylCIrdK/QqudkLVo7df0ArVGpxg0XFImMM4e2Q17ZQ/slj2zjz9rNYIavpcDmtWmljul2PHI+vu/KpNmgf0vVUvPAmXMBV518u4EjH8LramvH56+rs/nxhsT7Iq9kP9L9sTu6QZW/U27zvLcWQs/KnmhF6MGyT/b8RtsTqXkmdR0Np1cWg5b1Y1RjGGS+jGLJawhgzzVN3GCc1xIA1JaWpAWm6lSJNQM41tIq58ImpKf</latexit> f : X ! Y
  39. 39. / 41 38
  40. 40. / 41 39 NeurIPS 2020 ICML 2020, 2021 ICLR 2020, 2021 • Self-Supervised Graph Transformer on Large-Scale Molecular Data • RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist • Reinforced Molecular Optimization with Neighborhood- Controlled Grammars • Autofocused Oracles for Model-based Design • Barking Up the Right Tree: an Approach to Search over Molecule Synthesis DAGs • On the Equivalence of Molecular Graph Convolution and Molecular Wave Function with Poor Basis Set • CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models • A Graph to Graphs Framework for Retrosynthesis Prediction • Hierarchical Generation of Molecular Graphs using Structural Motifs • Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning • Reinforcement Learning for Molecular Design Guided by Quantum Mechanics • Multi-Objective Molecule Generation using Interpretable Substructures • Improving Molecular Design by Stochastic Iterative Target Augmentation • A Generative Model for Molecular Distance Geometry • GraphDF: A Discrete Flow Model for Molecular Graph Generation • An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming • Equivariant message passing for the prediction of tensorial properties and molecular spectra • Learning Gradient Fields for Molecular Conformation Generation • Self-Improved Retrosynthetic Planning • Directional Message Passing for Molecular Graphs • GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation • Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space • A Fair Comparison of Graph Neural Networks for Graph Classification • MARS: Markov Molecular Sampling for Multi-objective Drug Discovery • Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design • Learning Neural Generative Dynamics for Molecular Conformation Generation • Conformation-Guided Molecular Representation with Hamiltonian Neural Networks • Symmetry-Aware Actor-Critic for 3D Molecular Design
  41. 41. / 41 40 https://arxiv.org/abs/2102.06321 https://arxiv.org/abs/1911.10084
  42. 42. / 41 41 CC1CCNO1 Representation Learning … NCc1ccoc1.S=(Cl)Cl>>[RX_5]S=C=NCc1ccoc1 Task-Specific Head https://itakigawa.github.io/data/aipseminar_202107.pdf https://youtu.be/wtrVxZCXnPQ?t=813

理研AIPオープンセミナー 2021.7.14 動画 https://youtu.be/wtrVxZCXnPQ?t=813 AIPオープンセミナー https://aip.riken.jp/event-list/seminars/?lang=ja AIPWeb動画ライブラリ https://aip.riken.jp/video-list/

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